Differential Viral Dynamics by Sex and Body Mass Index During Acute SARS-CoV-2 Infection: Results From a Longitudinal Cohort Study
Year: 2023
Type: article
Study design: Observational Study
Abstract: BACKGROUND: There is evidence of an association of severe coroanavirus disease (COVID-19) outcomes with increased body mass index (BMI) and male sex. However, few studies have examined the interaction between sex and BMI on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral dynamics. METHODS: Participants conducted RT-PCR testing every 24-48 hours over a 15-day period. Sex and BMI were self-reported, and Ct values from E-gene were used to quantify viral load. Three distinct outcomes were examined using mixed-effects generalized linear models, linear models, and logistic models, respectively: all Ct values (model 1), nadir Ct value (model 2), and strongly detectable infection (at least 1 Ct value ≤28 during their infection) (model 3). An interaction term between BMI and sex was included, and inverse logit transformations were applied to quantify the differences by BMI and sex using marginal predictions. RESULTS: In total, 7988 participants enrolled in this study and 439 participants (model 1) and 309 (models 2 and 3) were eligible for these analyses. Among males, increasing BMI was associated with lower Ct values in a dose-response fashion. For participants with BMIs greater than 29 kg/m2, males had significantly lower Ct values and nadir Ct values than females. In total, 67.8% of males and 55.3% of females recorded a strongly detectable infection; increasing proportions of men had Ct values <28 with BMIs of 35 and 40 kg/m2. CONCLUSIONS: We observed sex-based dimorphism in relation to BMI and COVID-19 viral load. Further investigation is needed to determine the cause, clinical impact, and transmission implications of this sex-differential effect of BMI on viral load.
Source: Clinical Infectious Diseases
DOI: 10.1093/cid/ciad701
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: U.S. Department of Health and Human Services, Alzheimer's Association, American Heart Association, Bristol-Myers Squibb, Pfizer, Heart Rhythm Society, Johns Hopkins University, Quest Diagnostics, Moderna, European Commission, National Institutes of Health, Centers for Disease Control and Prevention, Center for AIDS Research, Duke University, Center for AIDS Research, University of Washington, National Institute on Aging, National Heart, Lung, and Blood Institute, National Institute of Allergy and Infectious Diseases, National Institute of Biomedical Imaging and Bioengineering, National Center for Advancing Translational Sciences
Awards: University of Massachusetts Center for Clinical and Translational Science, DIGITAL, RISK-BASED SCREENING FOR ATRIAL FIBRILLATION IN THE EUROPEAN COMMUNITY, Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches, G2502872260, Futurizing Brain Health Monitoring Platform, G4869932426, G5284908920, Institutional Career Development Core, J: NRSA Training Core, University of Pittsburgh Clinical and Translational Science Institute, The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep Diseases
OpenAlex ID: W4388801433
